Magnetic Resonance Imaging (“MRI”) systems generally use strong magnetic fields in order to polarize the magnetic spins of nuclei to be imaged, and to create the magnetic resonance condition therein.
Numerous clinical and research applications related to brain imaging using MRI, Computerized Tomography (“CT”), Positron Emission Tomography (“PET”), and Single Proton Emission Computerized Tomography (“SPECT”) require the ability to accurately extract the brain tissue from the image data. For example, for patients suffering from various brain disorders (such as traumatic injury, multiple sclerosis, or dementia), brain atrophy estimation, the rate of atrophy (the difference in brain volume at two points in time) and brain shape itself can provide important diagnostic information. Imaging parameters can be more sensitive and consistent measures of disease progression than cognitive assessment in patients with Alzheimer's disease. Imaging measures may be used to predict the course of disease and could be used as surrogate endpoints in treatment.
In addition to imaging of the brain, Magnetic Resonance (“MR”) and CT images of the head contain non-brain elements such as bone, skin, fat, muscle, and eyeballs. Multi-modality algorithms generally require that these non-brain parts of the image be removed before registration.
A segmentation of the brain tissue is one of the most time-consuming preprocessing steps performed in neuro-imaging laboratories. A number of brain extraction algorithms have been developed to perform this step automatically. Such conventional algorithms may generally enhance the speed of overall image processing, but still have various drawbacks.
One such conventional technique and system, i.e., Brain Extraction Tool (“BET”), utilizes an intensity based estimation of the brain-non-brain threshold, determines the center of gravity of the head, defines a starting sphere based on the center of gravity, and deforms the tessellated sphere outward toward the brain surface. Similarly to any intensity-only based method, the drawback of this conventional approach is low accuracy.
Another conventional technique and system, i.e., Brain Surface Extraction (“BSE”), utilizes an edge-based method based on an anisotropic diffusion filtering technique. The edge detection can be implemented using a 2D Marr-Hildreth operator. The final step of BSE includes morphological processing of the edge map. The drawback to such BSE approach is generally poor precision. Further, using a 2D edge detector on 3D data generally provides little benefit, since such algorithm may not exploit the correlation of edges across adjacent slices.
A further conventional technique and system, i.e., Minneapolis Consensus Strip (“McStrip”), is an iterative combination of the above two approaches. According to this procedure, the target mask is formed by the BET approach, and then an exhaustive search is performed to obtain the parameters of BSE that provide a BSE mask that is closest to the BET mask. Due, in part, to the iterative nature of this technique, the execution time of such approach generally makes it unsuitable for a routine use, even though there may be a good the precision.
Now, addressing traditional methods of colorectal screening, it has been previously discussed that the majority, e.g., 85-90%, of colorectal cancers progress through the benign adenoma-Carcinoma sequence, with an average of 5.5 years required for the transformation of a large adenomatous polyp into cancer. Colon cancer screening can decrease the mortality of colorectal cancer by identifying these pre-malignant lesions. Screening has been shown to decrease the morbidity and mortality by detecting and removal of pre-malignant adenomatous polyps. There is consensus among health care providers and policy makers that screening for colorectal cancer is justified. The conventional options available for colorectal carcinoma screening include digital rectal examination, fecal occult blood testing, sigmoidoscopy, double contrast barium enema, and fiberoptic colonoscopy.
Despite the consensus on the need and efficacy of screening, there are about 150,000 new cases and 60,000 deaths from colon cancer every year in the United States. Since screening can detect the precancerous adenomas, the continued high prevalence of colon cancer is alarming. Only 17.3% of patients over age 50 had undergone fecal occult blood testing within the last year and 9.4% had undergone sigmoidoscopy within the last three years. However, conventional colon screening options have important limitations. For example, fecal blood test does not directly evaluate the colonic mucosa. Many large adenomatous polyps and cancers do not bleed. In more than 50% of occult home positive stool examinations, the source of blood was from the upper gastrointestinal tract. Screening sigmoidoscopy generally fails to evaluate the entire colon, may miss many advanced proximal carcinomas. The sensitivity of barium enema examination in detecting polyps larger than 5 mm is only about 25% and 50% for polyps greater than 1 cm.
Complete fiber-optic colonoscopy allows for a thorough evaluation of the colon, and has the added benefit of biopsy or excision of suspicious lesions. However, there are several important limitations to the widespread use of screening colonoscopy including need for sedation, potential risk of perforation and bleeding, costs of the procedure including the need for sedation, failure to complete the examination in 5-10% of patients, and an insufficient workforce of trained endoscopists to meet the demand (15,16). One of important limitation of conventional colonoscopy is that in order to perform the examination, the colon must be thoroughly cleansed of residual fecal material. This is typically performed with polyethylene-glycol-solutions or phospho-soda preparations. Patients find bowel cleansing the most difficult aspect of screening, whether sigmoidoscopy, DCBE, or colonoscopy is used.
CT colonography (“CTC”) is an imaging technique for colorectal polyp detection that relies on performing thin sections computed tomography (“CT”) of the colon, and has been described in various publications. Preliminary clinical evaluation of CTC shows positive results in detecting polyps and cancers of the colon and rectum, with sensitivity values ranging from 75-100% for polyps that are at least 10 mm. CT and conventional colonoscopy has been evaluated for the detection of polyps in asymptomatic, average risk, patients. (See M. Makari et al., “Colorectal Polyps and Cancers in Asymptomatic Average-Risk Patients: Evaluation with CT Colonography,” Radiology 2004, Vol. 230, pp. 629-636.) It is suggested that CTC may be an accurate study in detecting clinically significant colorectal lesions in a screening population. The mean interpretation time may be about 9 minutes. Interpretation times in this range are important if CTC is to be used as a widespread screening tool. Studies evaluating patient preferences have shown CT colonography to be preferred over conventional colonoscopy. Data has shown that 70.5% of patients preferred CTC over conventional colonoscopy. However, the current CTC data are acquired after colonic cleansing for optimal data interpretation. Bowel cleansing may be a major impediment to widespread CTC, it is judged uncomfortable (e.g., by about 89% of patients) and inconvenient (e.g., by about 78% of patients). By eliminating the need for bowel cleansing, patient and physician acceptance of CTC as a colon cancer screening tool would likely substantially increase. If CTC was effective in detecting colorectal polyps, and did not require a bowel preparation, it could become the colorectal cancer screening test of choice.
Given the limitations of current bowel preparations, including poor patient compliance as well as residual fecal material that can make interpretation difficult, the possibility of fecal and fluid tagging for CTC has been investigated. Fecal tagging without bowel cleansing relies on having the patient ingest small amounts of dilute barium with low fat and fiber diets one to several days prior to the examination. When the CT examination is performed, residual fecal material that is tagged may have high attenuation and appear brighter on the image. If there are large amounts of residual “tagged” fecal material present, clinically significant polyps could be obscured. Utilizing segmentation techniques it is possible to remove tagged fecal material leaving only the colonic mucosa, polyps, and colorectal neoplasms.
Several studies evaluated fecal tagging. The software for implementing such technique is based on replacing CT pixels with attenuation greater than 200 HU with “air” (−1,000 HU), followed by selective smoothing of a 3-pixel-thick transition layer at the bowel wall-air interface. (See M E Zalis et al., “CT Colonography: digital subtraction bowel cleansing with mucosal reconstruction-initial observations,” Radiology 2003, Vol. 226, pp. 911-917). If a polyp is surrounded by residual fecal material, this software modifies a 2 mm thick surface layer of the polyp. This is an undesirable side effect. Moreover, a purely threshold-based technique is clearly unable to remove incompletely tagged fecal matter. This is a limitation, since despite the best effort to tag fecal material, there are always be some poorly or partially tagged fecal matter remaining. Thus, a need exists to provide techniques to remove both tagged and untagged fecal matter from the colon.
Therefore, there is a need to be able to perform a segmentation of brain tissue and of other biological matter with a high precision and a relatively short execution time.